Every time a disruptive technology becomes available, humanity goes through essentially the same cycle: At first, exaggerated fear and sulking rejection on the one side clash with overflowing enthusiasm on the other. As the technology matures and its capabilities and limitations become more distinct, eventually a synthesis—often grounded in rules and regulations about where and how it can be most beneficially applied—emerges. Thus, what once seemed new, outlandish, and groundbreaking turns into a commodity, and often one that helps to shape the next level of innovation. The question about generative AI today is obvious: “Will this time be different?”

As a kid, I’ve enjoyed stories in which inanimate objects suddenly came to life, either through magic or with the help of science. Mary Shelley’s Frankenstein, for example. Or that old Jewish myth of the Golem: A figure, formed of clay, is brought to life by a Rabbi in order to liberate the holy man of his most tedious chores. No more cooking, cleaning, chopping wood, or carrying water. More time instead for leisure, study, and prayer. But, as with Frankenstein’s monster, that endeavor doesn’t end well for either of them: The Golem eventually turns against its creator in a fury of destructive violence—before finally being shut down.

Marc Andreessen’s recent article “Why AI Will Save the World” is—sadly—an example of how polarized society and public discourse have become, particularly on such high-stakes topics as the opportunities and risks posed by the proliferation of AI. While I largely applaud (and mostly agree with) his optimism about the tremendous potential of AI to improve virtually every aspect of human life and flourishing, some of his arguments against AI regulation are not only absurd, but threaten to unnecessarily poison this important debate.

Tracking the search engine market feels almost mesmerizing these days—a bit like staring into a lava lamp and wondering what’s going on in there. After Google having dominated the field practically forever, with Microsoft’s Bing struggling to even register as a contestant, competition apparently surged and almost immediately plummeted again. At first, it seemed as if Google had overlooked the potential disruption of the traditional, stateless search interface by conversational and interactive experiences that ChatGPT and others offer. Nevertheless, now that OpenAI, Microsoft’s “new” Bing, and newcomers such as you.com have attracted a lot of eyeballs, Google is expected to rush its Bard product to the market in an urgent attempt to catch up. But despite the broader question wether this launch frenzy will lead to an “AI arms race” with unforeseeable consequences, a more heterogenous search market has many upsides: At the end of the day, consumers are likely to get better search experiences of this, regardless which tool they prefer to use. And advertisers will have a real choice about which platforms they want to appear on—without forfeiting the lion’s share of web traffic if they’re not comfortable with Google Ads.

Language is arguably humanity’s most consequential invention. Unlike any other species, Homo Sapiens has developed a means by which complicated, abstract plans can be efficiently and effectively transmitted from one mind to the next. Thus, our prehistoric ancestors, in contrast to their natural rivals, could successfully coordinate, say, a large-scale hunt, an attack on a foreign tribe, or even how they wanted to arrange life in increasingly complex social communities. Language also enabled the accumulation of knowledge over time: Ideas and discoveries could be handed down from one generation to the next. Verbally at first, later also in writing, allowing for the vast, collective wisdom to which we have access today to emerge. But I’m afraid that the exceptional role that language plays for us humans has caused a great deal of confusion—not only about how our own minds work, but also about the nature of intelligence in general.

The advent of ChatGPT and the subsequent release of GPT-4 have brought a wave of publicity to the field of generative AI. Many observers marvel at the levels of “creativity” that the technology has attained seemingly overnight and it’s tempting to get lost in pondering its implications on the future of, say, education, white-collar work, healthcare, or any other sector of the economy that suddenly looks ripe for disruption. However, we should not lose sight of the very real challenges posed by an adjacent category of artificial intelligence, namely the one that’s already deeply entrenched in our lives: The “merely” predictive algorithms that filter our inboxes, match us up with romantic partners, curate our news feeds, and slect the content we will watch or listen to next.

If you’re reading this, the mounting news of apparent breakthroughs in generative AI have surely not passed you by: Tools that can compose music, write code, paint pictures, and genuinely seem to be smart enough to pass various state exams have become publicly (and mostly freely) available in a surprisingly short timespan. This wave of commoditization has also shed the light of public discourse onto debates about AI safety which had, until now, been confined to relatively small, mostly academic circles. A TIME magazine cover story referred to big tech’s sudden rush to bring these technologies to the market as an “AI arms race”. Valid concerns about security, safety, privacy, and ethics, the authors warn, might quickly be thrown overboard in the interest of growth in shareholder value.

Designing Your Work Life by Bill Burnett and Dave Evans. “F**k this s**t, I’m quitting!” Many of us have played with the thought of making a memorable exit from a job we didn’t enjoy at some point in our careers. I know a few people who actually did (though not with quite such strong language), but unsurprisingly that didn’t end well for any of them. In their new book Designing Your Work Life, Stanford professors Bill Burnett and Dave Evans explore not only how to quit well (spoiler: avoid the f-word if you can!), but also how Design Thinking tools can help us draw more satisfaction from our current jobs, thus reducing the need for quitting in the first place.

In many instances software architecture is less a product of deliberate design and more an accidental result of countless interactions between people. Or, as Eric Raymond put it more pointedly: “If you have four groups working on a compiler, you’ll get a 4-pass compiler.” How Do Committees Invent? by Mel Conway. This idea was originally introduced by Mel Conway in his 1967 paper “How Do Committees Invent?” and has since turned into what is now known as Conway’s Law:

The other day I had a wonderful conversation with a friend and fellow software enthusiast about fairness when it comes to the remuneration of knowledge workers. Both of us have been through various posts in different technology companies, increasingly concerning ourselves with management and leadership as our careers progressed. Our conversation honed in on the following question: Is the way people are payed in our industry today fair? Note that I’m not referring only to software developers here—data scientists, UI experts, DevOps heroes, product managers, and all the others who contribute to successful projects are included as well.